Heart rate sensors are well known to monitor or detect vital signs like a heart rate of a user. Such a heart rate sensor can be an optical heart rate sensor based on a photo-plethysmographic (PPG) sensor and can be used to acquire a volumetric organ measurement. By means of PPG sensors, changes in light absorption of a human skin is detected and based on these measurements a heart rate or other vital signs of a user can be determined. The PPG sensors comprise a light source like a light emitting diode (LED) which is emitting light into the skin of a user. The emitted light is scattered in the skin and is at least partially absorbed by the blood. Part of the light exits the skin and can be captured by a photodiode. The amount of light that is captured by the photo diode can be an indication of the blood volume inside the skin of a user. A PPG sensor can monitor the perfusion of blood in the dermis and subcutaneous tissue of the skin through an absorption measurement at a specific wavelength. If the blood volume is changed due to the pulsating heart, the scattered light coming back from the skin of the user is also changing. Therefore, by monitoring the detected light signal by means of the photodiode, a pulse of a user in his skin and thus the heart rate can be determined. Furthermore, compounds of the blood like oxygenated or de-oxygenated hemoglobin as well as oxygen saturation can be determined.
FIG. 1 shows a basic representation of an operational principle of a heart rate sensor. In FIG. 1, a heart rate sensor 100 is arranged on an arm of a user. The heart rate sensor 100 comprises a light source 110 and a photo detector 120. The light source 110 emits light onto or in the skin 1000 of a user. Some of the light is reflected and the reflected light can be detected by the photo detector 120. Some light can be transmitted through tissue of the user and be detected by the photo detector 120. Based on the reflected or transmitted light, vital signs of a user like a heart rate can be determined.
The results of a heart rate sensor can be used to estimate or measure a caloric Activity Energy Expenditure AEE.
FIG. 2 shows a representation of a total energy expenditure of a human being. The Total Energy Expenditure TEE is composed of a Basal Energy Expenditure BEE, a diet induced thermogenesis DIT and an Activity Energy Expenditure AEE. The Basal Energy Expenditure BEE is a combination of the sleeping metabolic rate and the energy expenditure from arousal.
If a user wants to for example reduce his weight, he must burn more calories than he is eating or drinking. The Activity Energy Expenditure AEE is that part of the Total Energy Expenditure TEE which is influenced by the activity of the person.
When a user is trying to reduce weight, it is often not easy for the user to determine how many calories he has spent throughout an activity or workout. Hence, there is a need for an accurate estimation or measurement of the energy spent during an activity.
An accurate estimation or measurement of the caloric Activity Energy Expenditure AEE is therefore an important factor for example for smart watches enabling sport and wellbeing applications.
Accordingly, it is desired to provide a monitor which can monitor the activity of a user during the day and which can measure or estimate the Activity Energy Expenditure, i.e. the energy expenditure of a user during a day.
FIG. 3 shows a graph indicating an Activity Energy Expenditure AEE prediction as a function of a measured Activity Energy Expenditure. In FIG. 3, the measured Activity Energy Expenditure MAEE is depicted at the X-axis and the predicted Activity Energy Expenditure PAEE is depicted at the Y-axis. Furthermore, in FIG. 3, several activity types like running, cycling, rowing, using a cross trainer etc. are depicted as data points. For some activities like walking, an overestimation OE (the estimated Activity Energy Expenditure AEE is too high) can be present. For other activities like cycling, rowing and using a cross trainer, an underestimation UE (the estimated Activity Energy Expenditure AEE is too low) can be present. In FIG. 3, furthermore, the optimal estimation OES is also depicted, namely the situation where the measured Activity Energy Expenditure MAEE corresponds to the predicted Activity Energy Expenditure.
The reasons why the measured and predicted Activity Energy Expenditure do not correspond to each other can be that the model based on which the predicted Activity Energy Expenditure AEE is determined is not accurate enough or the activity which the user is performing is not reflected good enough in the model.
According to FIG. 3, some activities requiring a high physical activity level may be misinterpreted or underestimated like cycling, rowing and using a cross trainer. In addition or alternatively to using heart rate data, the Activity Energy Expenditure AEE can be determined or estimated for example based on motion data of a user acquired from an acceleration sensor.
If a heart rate of a user is used to estimate the Activity Energy Expenditure AEE, it should be noted that the known linear relationship between the heart rate and the energy expenditure is only valid for aerobic activities with a moderate or vigorous exertion level. Furthermore, heart rate data which is measured for example during mental stress and fatigue may cause a biased prediction output in particular for low intensity activities. Furthermore, motion artifacts may be present in heart rate data. These motion artifacts may in particular occur during activities, which show an unpredictable thus non-repetitive movement pattern. Examples of such movements are several normal daily activities when full body motion is not represented by hand and wrist movement.
Furthermore, it should be noted that Activity Energy Expenditure AEE can be predicted quite accurately based on heart rate data during aerobic activities while acceleration and movement information are most suitable to predict Activity Energy Expenditure during sedentary, low intensity activities with a low exertion level or non-structured activities.
WO 2014/207294 A1 discloses a system for monitoring physical activity based on monitoring motion data of a user or alternatively based on a heart rate activity of a user.
EP 1 424 038 A1 discloses a device for measuring a calorie expenditure.
US 2008/0139952 A1 discloses a biometric information processing device which can display a calorie expenditure.
S. Brage. “Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure”, Journal of Applied Physiology, vol. 96, no. 1, 29 Aug. 2003, pages 343-351, discloses a vital signs monitoring system which computes an overall activity energy expenditure of a user. Static weight coefficients are determined offline during a training phase of a system. This is in particular performed by minimizing the root-mean-square error between a reference physical activity energy expenditure and an estimated activity energy expenditure derived from a mode with weight coefficients.